Dynamic analysis of stochastic bidirectional associative memory neural networks with delays
Dynamic analysis of stochastic bidirectional associative memory neural networks with delays
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DOI：
10.1016/j.chaos.2005.12.010
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发表时间：
2007-06
影响因子：
7.8
通讯作者：
Hongyong Zhao;Nan Ding
中科院分区：
文献类型：
--
作者：
Hongyong Zhao;Nan Ding
研究了一类随机时滞双向联想记忆神经网络模型。通过构造李雅普诺夫泛函，利用随机分析方法和不等式技巧，给出了几乎必然指数稳定、p次指数稳定和均值指数稳定的充分判据.所得到的判据可为实际应用中考虑随机噪声时神经网络的镇定提供理论指导。
In this paper, stochastic bidirectional associative memory neural networks model with delays is considered. By constructing Lyapunov functionals, and using stochastic analysis method and inequality technique, we give some sufficient criteria ensuring almost sure exponential stability, pth exponential stability and mean value exponential stability. The obtained criteria can be used as theoretic guidance to stabilize neural networks in practical applications when stochastic noise is taken into consideration.